Pharmacogenetic Algorithm for Individualized Controlled Ovarian Stimulation (iCOS) in Assisted Reproductive Technology Cycles
Pharmacogenetic Algorithm for Individualized Controlled Ovarian Stimulation (iCOS) in Assisted Reproductive Technology Cycles
In this study, the investigators will evaluate genetic variation or polymorphisms between individuals submitted to IVF/ICSI cycles, and how these variations influence response to COS and pregnancy outcomes. Thus, this project has the aim to develop a pharmacogenetic algorithm associating gene polymorphisms, the patient clinical information, and functional and hormonal biomarker to: 1) predict the patient response to gonadotropin; 2) develop individualized gonadotropin regimens; 3) improve the accuracy of determining appropriate dosages of gonadotrophins; 4) improve efficacy and patient compliance to COS; and finally 5) reduce the time-to-pregnancy.
Controlled ovarian stimulation (COS) is crucial for optimizing in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) success. Multiple factors influence the ovarian response to COS, making predictions about oocyte yields not so straightforward. As a result, ovarian response may be poor or suboptimal, or even excessive, all of which has negative consequences to the affected patient. The study of gene polymorphisms regulating the female reproductive function may help to clarify the mechanisms responsible for gonadal function and fertility, and also the inter-individual variability in ovarian response to COS.
The main objective of COS is obtaining an adequate number of good quality oocytes, and also minimize the adverse drug reactions and cycle cancellations due to a high or poor response. The ideal number of oocytes to optimize treatment outcomes has been subject of debate for many years. However, based on recent published data, it seems reasonable to define the optimal number of oocytes to be retrieved after an adequate COS as 10-15 oocytes. To perform an adequate COS, it has been suggested that the antral follicle count (AFC) and anti-müllerian hormone (AMH) are the best biomarkers to predict the ovarian response. Recently, two nomograms have been proposed for the calculation of the follicle stimulating hormone (FSH) starting dose for COS, taking into account different parameters, namely: 1) Age, AFC and day 3 serum FSH; and 2) Age, serum AMH and FSH. Meanwhile, these prediction models are not able to predict an unexpected poor response and also a higher and undesirable ovarian response to FSH monotherapy.
Considering the number of retrieved oocytes, it has been recently purposed a new patient stratification during ART treatments: poor response (1-3 oocytes); sub-optimal response (4-9 oocytes); normal response (10-15 oocytes); high response (>15 oocytes). The reason to create this new category of sub-optimal responder is that they have poorer outcomes during ART treatments when compared to patients that have 10-15 oocytes retrieved. This category of sub-optimal responder has led to the introduction of new concept in reproductive medicine with the objective to stratify low prognosis patients undergoing ART based on the combination of quantitative and qualitative parameters. There is a group of patients that although present normal biomarkers of ovarian reserve, such as AFC and AMH, they have a sub-optimal response to COS. They have 9 retrieved oocytes, showing the inadequacy of using only the traditional ovarian reserve biomarkers to predict the ovarian response. This sub-optimal response should be related to ovarian sensitivity to exogenous gonadotrophins modulated by genetic factors.
Hence, adoption of a pharmacogenetic approach in assisted reproduction seems attractive as it may help understanding the relationship between genetic variants and ovarian response to exogenous gonadotropins. The patient´s genetic profile could be used to select the most appropriate gonadotropin type, predict the optimal dosage for each drug, develop cost-effective treatment plan, maximize the success rates, and finally decrease the time-to-pregnancy.
The study of genetic polymorphisms is undoubtedly a promising field in reproductive medicine. The candidate genes that play a role in the ovarian response to COS are as follows: i) genes that affect follicular function by exerting a hormonal effect - FSH, FSHR, V-LH, AMH, AMHR2, ERα, ERβ, CYP17, CYP19, COMT, MTHFR, GnRH1, KISS1, and KISS1R; ii) genes that affect the rate of initial primordial follicle recruitment in relation to the pool of growing follicles - BMP15, GDF9, and FOXL2; and iii) genes that encode DNA binding proteins and transcription factors, such as LHX8 and NANOS3. Variations in these genes can determine the variability of the follicular pool and explain the variance of the COS response and the results of ART.
In modern reproductive medicine, where the individualization and personalized treatments should be the norm, optimizing and obtaining the best result in each COS would be of great interest to both clinicians and patients alike. Considering the variability of patients´ response to COS, it is clear inadequate to select the type of gonadotropin and its initial dose empirically or based only on clinical and hormonal parameters.
In this study, the investigators will evaluate genetic variation or polymorphisms between individuals submitted to IVF/ICSI cycles, and how these variations influence response to COS and pregnancy outcomes. Thus, this project has the aim to develop a pharmacogenetic algorithm associating gene polymorphisms, the patient clinical information, and functional and hormonal biomarker to: 1) predict the patient response to gonadotropin; 2) develop individualized gonadotropin regimens; 3) improve the accuracy of determining appropriate dosages of gonadotrophins; 4) improve efficacy and patient compliance to COS; and finally 5) reduce the time-to-pregnancy.
Inclusion Criteria:
Exclusion Criteria:
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